GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning

• URL: http://arxiv.org/abs/2511.15256v1
• Date: Wed, 19 Nov 2025 09:19:39 GMT
• Title: GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
• Authors: Yanchen Xu, Ziheng Jiao, Hongyuan Zhang, Xuelong Li,
• Abstract summary: We propose Group Relative Policy Optimization for Representation Model (GRPO-RM)<n>Our method establishes a predefined output set to functionally replace token sequence sampling in large language models (LLMs)<n>A specialized reward function is designed to accommodate the properties of representation models.
• Score: 52.16150076582931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Group Relative Policy Optimization (GRPO), a reinforcement learning method used to fine-tune large language models (LLMs), has proved its effectiveness in practical applications such as DeepSeek-R1. It raises a question whether GRPO can be generalized to representation learning models. In this paper, we propose Group Relative Policy Optimization for Representation Model (GRPO-RM), and investigate the performance of GRPO-like policy in post-training representation models. Specifically, our method establishes a predefined output set to functionally replace token sequence sampling in LLMs, thereby generating an output group, which is essential for the probability-driven optimization of GRPO. In addition, a specialized reward function is designed to accommodate the properties of representation models. Extensive experiments are conducted on various real-world datasets to validate the effectiveness of our proposed method.

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